To forget is to preserve: Machine Unlearning for 3D medical image segmentation

arXiv:2606.16180v1 Announce Type: cross Abstract: With new data privacy laws such as the General Data Protection Regulation (GDPR) [1] that allow individuals to ask that any of their personal information be erased from trained machine learning models, there has been a push to investigate the unlearning of data from models as a way to comply with these laws. In this regard, based on four mechanics, we consider several approximate unlearning strategies applied to the MRBrainS18 dataset [2]. We use a 3D ResNet-50 [3] as a backbone architecture for segmentation that has been pre-trained with the M

Source: arXiv cs.LG — read the full report at the original publisher.

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